Reliability Of System Components Research Articles

Research on the Application of Multi-Physics Field Coupling Simulation in the Analysis of Temperature Field of 500kv GIS Circuit Breaker

Gas insulated switchgear (GIS) is a crucial part of any electrical infrastructure. Overheating the conductor on the GIS busbar would reduce the effectiveness of the insulating gas and shorten the life of the device. We then maximize output by fine-tuning the busbar's rotation angle, central distance, and conductor thickness. Dependable electrical systems always include gas insulated transmission lines (GIL) and gas insulated switchgear (GIS). Because of their reduced total cost and greater efficiency in terms of both space use and power transmission, GILs are gradually replacing conventional overhead lines for long-distance, high-power transmission. The reliability of GIL performance and the ease with which designs can be optimized necessitates research into their power loss and temperature profiles. The study found that the maximum temperature and power loss variances were approximately 70% attributable to differences in conductor thickness. By employing the aforementioned combination method (A1, B5, C5), energy consumption and thermal load in the GIS can be reduced. The research also shows that SF6 gas retains its outstanding insulating capacity after undergoing structural optimization. The calculation of the gas breakdown margin demonstrates this. In conclusion, the results of this study contribute to the understanding of heat transmission in three-phase GIS busbars and their optimization and engineering. Examining the temperature field of 500kV GIS circuit breakers, it highlights the value of multi-physics field coupling simulations in improving the reliability and performance of power system components, especially GILs. This is done so that power system components are more reliable and run more efficiently. Optimal heat transmission and other design considerations for three-phase GIS busbars are the most important takeaways from this research.

The concept of diagonal approximated signature: new surrogate modeling approach for continuous-state systems in the context of resilience optimization

The increasing size and complexity of modern systems presents engineers with the inevitable challenge of developing more efficient yet comprehensive computational tools that enable sound analyses and ensure stable system operation. The previously introduced resilience framework for complex and sub-structured systems provides a solid foundation for comprehensive stakeholder decision-making, taking into account limited resources. In their work, a survival function approach based on the concept of survival signature models the reliability of system components and subsystems. However, it is limited to a binary component and system state consideration. This limitation needs to be overcome to ensure comprehensive resilience analyses of real world systems. An extension is needed that guarantees both maintaining the existing advantages of the original resilience framework, yet enables continuous performance consideration. This work introduces the continuous-state survival function and concept of the Diagonal Approximated Signature (DAS) as a corresponding surrogate model. The proposed concept is based on combinatorial decomposition adapted from the concept of survival signature. This allows for the advantageous property of separating topological and probabilistic information. Potentially high-dimensional coherent structure functions are the foundation. A stochastic process models the time-dependent degradation of the continuous-state components. The proposed approach enables direct computation of the continuous-state survival function by means of an explicit formula and a stored DAS, avoiding costly online Monte Carlos Simulation (MCS) and overcoming the limitation of a binary component and system state consideration during resilience optimization for sub-structured systems. A proof of concept is provided for multi-dimensional systems and an arbitrary infrastructure system.

Modeling the Failure Rate of a Standby Multi-Component System and Improving Reliability

Evaluating the reliability of a system requires knowledge of the failure modes to which it is subject. Complex topology systems generally require a high level of availability, which is a function of the arrangement of elements (components) in the system. To avoid serious failures for such complex systems, recourse can be had to the redundancy techniques available in the literature. These techniques help improve system reliability, without affecting the reliability of system components. This paper is interested in the proposal of a model for evaluating the failure rate of a standby multi-component system and in improving the reliability of mechanical systems, arranged in a topology (series, parallel, or mixed).

BAYESIAN NETWORKS FOR RELIABILITY ANALYSIS OF TECHNICAL SYSTEM

The works to provide reliability assurance and enhancement are most effective at the early stages of life cycle. The common requirement is fast and precise performance of reiterated reliability analysis, as far as the system design development and the complete data accumulation on system components reliability. Recently, Bayesian networks has been introduced in reliability engineering. Bayesian networks are a promising tool for reliability analysis. Today, Bayesian networks are actively used to fault diagnosis and reliability estimates. In this article, utilization of the Bayesian networks for reliability analysis at the early stages of life cycle is studied. The reliability can be calculated by converting block diagrams or fault trees into Bayesian networks. Fault trees can easily be converted into Bayesian networks. Therefore, in this article, a perspective approach to combine the Bayesian networks mechanism and Fault tree analysis method is studied for reliability assessment of a technical system. The system is an electronic fire sensor. As a result, the developed model of Bayesian networks provides the estimation of failure probability at the conditions of real-life scenarios of system behavior; the calculation of the probability of no-failure operation of the system under control and its sub-systems; the detection of the elements that contribute the most to the failure probability. The Bayesian networks model can be easily built from the circuit diagram analysis and the FMEA (Failure Modes and Effects Analysis). In general, this method has the best capability in this real instance compared to other methods.

Метод и информационная технология для поддержки принятия решений по оценке надежности и отказоустойчивости информационных систем

This article is devoted to assessing the reliability and fault tolerance of information and automated systems. A method is proposed that allows one to determine the level of fault tolerance of components of information and automated systems, to take into account several criteria with different directions of optimization, to evaluate and rank alternative solutions. Based on the proposed method, corresponding information technology has been developed, which makes it possible to determine the reliability of the operation of information and automated systems. The proposed method and approach were applied in practice to assess the reliability and fault tolerance of information system components according to various criteria. The levels of fault tolerance of each of the components of the information system were identified. Practical application of the method has shown that it can be used to calculate the level of reliability and fault tolerance of information and automated systems.

Satisfaction with and reliability of in-hospital video-EEG monitoring systems in epilepsy diagnosis – A German multicenter experience

ObjectiveTo analyze satisfaction with and reliability of video-electroencephalography-monitoring systems (VEMS) in epilepsy diagnostics. MethodsA survey was conducted between December 2020 and January 2021 among German epilepsy centers using well-established customer satisfaction (CS) and quality assurance metrics. ResultsAmong 16 participating centers, CS with VEMS was low, with only 13% of customers actively recommending their system. Only 50% of users were satisfied with the overall performance of their VEMS, and a low 18% were satisfied with the manufacturer’s customer service. User interface, software stability, lack of regular updates, and missing customer-oriented improvements were reported as frequent problems jeopardizing diagnosis in approximately every 10th patient. The greatest potential for improvement was identified for software and hardware stability as well as customer service. ConclusionSatisfaction with VEMS and their customer service was low, and diagnostics were regularly affected by software or hardware errors. Even if this can be partly explained by the technical complexity of VEMS, there is an urgent need for improvements with regard to the reliability and durability of system components as well as signal synchrony and data management. SignificanceThis analysis highlights low consumer satisfaction of users with VEMS and uncovers frequent problems and potential for improvement.

Optimization and estimation in system reliability allocation problem

Designing an optimal reliability system with a minimum cost is one of the challenging problems in engineering field over the years. Estimating the subsystem reliability and determining the optimal number of component allocation will give an insight into the adequate performance of the entire system under study. In some scenarios, if a subsystem fails, then the whole system will fail, thus posing a severe threat to the safety of engineers and personnel involved. Hence, the need to optimally determine and allocate the required number of components as well as estimating their reliability so that the system’s safety would be guaranteed. In this paper, two procedures for estimating system reliability function are proposed using the maximum likelihood estimators and uniformly minimum variance unbiased estimators. The reliability functions of some selected lifetime distributions are estimated using the proposed procedures via a simulation study. The study presents a hybrid concept of estimation and optimization theory in reliability allocation problem. The work illustrated through a case study, and an optimization technique used to determine the optimal number of components at a minimum cost. The study would be useful for decision-makers in heavy industries/complex systems to help estimate the reliability of system components, optimize the allocation and manufacturer selection problems.

Investigation and feasibility study of using components with different categories from the perspective of radiation damage in LEO and GEO orbits

Space radiation can affect the performance and reliability of components in space systems. This paper focuses on the investigation of three types of radiation damage including ionizing dose, displacement damage, and single event damage using OMERE software. Considering the outputs of this software, how to use and use a variety of electronic components with different commercial, military and space grades in LEO and GEO satellites is discussed. These components have the least risk of displacement damage. Mass budget constraints should also be considered when using commercial components in the GEO circuit. The maximum thickness for the safety of components in LEO and GEO circuits is 2.6 mm and 9.5 mm respectively. Given the inability of SEE damage to increase in thickness, the best solution to this damage is to use radiation-resistant solutions, especially software issues.

Big data reliability: A critical review

Big data requires new technologies and tools to process, analyze and interpret the vast amount of high-speed heterogeneous information. A simple mistake in processing software, error in data, and malfunctioning in hardware results in inaccurate analysis, compromised results, and inadequate performance. Thus, measures concerning reliability play an important role in determining the quality of Big data. Literature related to Big data software reliability was critically examined in this paper to investigate: the type of mathematical model developed, the influence of external factors, the type of data sets used, and methods employed to evaluate model parameters while determining the system reliability or component reliability of the software. Since the environmental conditions and input variables differ for each model due to varied platforms it is difficult to analyze which method gives the better prediction using the same set of data. Thus, paper summarizes some of the Big data techniques and common reliability models and compared them based on interdependencies, estimation function, parameter evaluation method, mean value function, etc. Visualization is also included in the study to represent the Big data reliability distribution, classification, analysis, and technical comparison. This study helps in choosing and developing an appropriate model for the reliability prediction of Big data software.

DC is the Future [Point of View

For ac electricity grids, the aim has always been at low-cost and disruption-proof solutions. The power grid is inherently vulnerable to environmental disturbances. However, the introduction of power electronics (PEs) and changes in load and generator characteristics have introduced a game changer at an unprecedented pace over the last decade. During this change, load specifications, load characteristics, and control and monitoring requirements for power system security have also dramatically evolved. Efficiency and reliability of system components have progressed in parallel with this evolution. However, high penetration of renewable energy sources has reduced the percentage contribution of conventional electricity based on synchronous generation, which affects grid stability and hence reliability. Furthermore, numerous camouflaged dc generators and dc loads are being embedded in ac and dc microgrids at ever increasing rates. While embedded generation rapidly increases its share in the hybrid power grid (where ac and dc coexist), the current developments in PE switches—based on wide bandgap (WBG) technology—indicate that within the next two decades Max Planck’s well-known sentiment that science advances one funeral at a time will repeat itself on the ac grid. This article aims to summarize the technical developments and problems facing the utilization of ac during widescale electrification of the world using numerous distributed energy resources (DERs). Then, it will map out evolutionary changes toward a future dc grid.

АНАЛИЗ ПОДХОДОВ К ОБЕСПЕЧЕНИЮ БЕЗОПАСНОСТИ ОБЛАЧНЫХ СЕРВИСОВ

The article is devoted to the analysis of modern approaches that ensure the security of cloud services. Since cloud computing is one of the fastest growing areas among information technology, it is extremely important to ensure the safety and reliability of processes occurring in the clouds and to secure the interaction between the client and the provider of cloud services. Given that fears about data loss and their compromise are one of the main reasons that some companies do not transfer their calculations to the clouds. The object of research and analysis of this work are cloud services, which are provided by various cloud service providers. The aim of the study of this work is to compare existing approaches that provide information security for cloud services, as well as offer a new approach based on the principle of diversity. There are many approaches that ensure their safety, using both traditional and cloud-specific. The multi-cloud approach is one of the most promising strategies for improving reliability by reserving cloud resources on the servers of various cloud service providers. It is shown that it is necessary to use diversity to ensure the reliability and safety of critical system components. The principle of diversity is to use a unique version of each resource thanks to a special combination of a cloud computing provider, the geographical location of data centers, cloud service presentation models, and cloud infrastructure deployment models. The differences between cloud providers and which combination of services are preferable to others in terms of productivity are discussed in detail. In addition, best practices for securing cloud resources are reviewed. As a result, this paper concludes that there is a problem of insufficient security and reliability of cloud computing and how to reduce threats in order to avoid a common cause failure and, as a result, loss of confidential data or system downtime using diversity of cloud services.

DESIGNING A SECURE BLOCKCHAIN-BASED TRADING PLATFORM FOR INTERNET OF THINGS

Blockchain, a distributed database technology attracts intensive attention recently. It was first described in 1991 by Stuart Haber and W. Scott Stornetta, in order to prevent tampering for document timestamps. After that, Satoshi Nakamoto conceptualized this technology with his work, a peer-to-peer electronic cash system named as Bitcoin. Due to its authenticity, user anonymity and data immutability; this technology has also been applied to different areas ranging from finance, supply-chain managements to social services. In this paper, we propose a blockchain based application for trading data, collected from IoT devices. Our application enables the sellers to commercialize their data in exchange of the currency produced by the application. The application also utilizes smart contracts to establish trust between both sides of the exchange. Furthermore, we discuss the security and reliability of the system components

A cost minimisation model for system reliability allocation

PurposeThe purpose of this paper is to consider a nonlinear problem of minimizing the cost of providing reliable systems. The authors assume that the system consists of several components in series, and for each such component, the cost of the component increases exponentially with its reliability.Design/methodology/approachIn order to solve this nonlinear optimization problem, the authors propose two approaches. The first approach is based on the concept of adjusting the reliability of a pair of components to minimize the cost of the system. The authors call this procedure as reliability adjustment routine (RAR). Proofs of optimality and convergence for the proposed model are also provided. The second approach solves the problem by using a Lagrangian multiplier. A procedure is developed to obtain the maximum step size to achieve the desired optimal solution in minimum iterations. Proposed approaches are efficient and give exact solutions.FindingsProposed methods enable a decision maker to allocate reliability to the components in series while minimizing the total cost of the system. The developed procedures are illustrated using a numerical example. Although an exponential relationship between the component cost and reliability is assumed, this can be extended to various other nonlinear distributions.Originality/valueThis cost optimization problem, subject to system component reliability values, assumes the near practical nonlinear pattern of cost vs reliability. Such problems are complex to solve. The authors provide a unique approach called RAR to solve such convoluted problems. The authors also provide an approach to solve such problems by using a Lagrangian multiplier method. Various proofs have been worked out to substantiate the work.

Emergent Failures: Rethinking Cloud Reliability at Scale

Since the conception of cloud computing, ensuring its ability to provide highly reliable service has been of the upmost importance and criticality to the business objectives of providers and their customers. This has held true for every facet of the system, encompassing applications, resource management, the underlying computing infrastructure, and environmental cooling. Thus, the cloud-computing and dependability research communities have exerted considerable effort toward enhancing the reliability of system components against various software and hardware failures. However, as these systems have continued to grow in scale, with heterogeneity and complexity resulting in the manifestation of emergent behavior, so too have their respective failures. Recent studies of production cloud datacenters indicate the existence of complex failure manifestations that existing fault tolerance and recovery strategies are ill-equipped to effectively handle. These strategies can even be responsible for such failures. These emergent failures-frequently transient and identifiable only at runtime-represent a significant threat to designing reliable cloud systems. This article identifies the challenges of emergent failures in cloud datacenters at scale and their impact on system resource management, and discusses potential directions of further study for Internet of Things integration and holistic fault tolerance.

OPTIMAL DG PLACEMENT IN A RADIAL DISTRIBUTION NETWORK FOR RELIABILITY IMPROVEMENT

This paper presents an efficient Grey Wolf Optimization technique to improve the reliability of distribution systems. Various reliability objectives such as energy not supplied, System Average interruption Frequency Index and System Average Interruption Duration Index are solved using the Grey Wolf Optimization method for optimal placement of distributed generators in radial feeders for reliability enhancement. Distributed generation sources are becoming more prominent in distribution systems due to the incremental demands for electrical energy and sometimes provide the lowest cost solution to handling low-voltage or overload problems. Siting and sizing of Distributed generation sources have profoundly impacted on the system losses in a distribution network. Assessment of customer power supply reliability is an important part of distribution system operation and planning. Distribution system reliability assessment is able to predict the interruption profile of a distribution system based on system topology and component reliability data. Distributed generation is being adopted in distribution networks with one of the objectives being enhancement of system reliability. Grey Wolf Optimization technique is proposed to obtain the optimal location DG to improve the reliability. The effectiveness of the proposed objective functions has been investigated on standard test distribution systems (69 bus radial distribution systems).

Integrated predictive maintenance strategy for manufacturing systems by combining quality control and mission reliability analysis

Predictive maintenance (PdM) is an effective means to eliminate potential failures, ensure stable equipment operation and improve the mission reliability of manufacturing systems and the quality of products, which is the premise of intelligent manufacturing. Therefore, an integrated PdM strategy considering product quality level and mission reliability state is proposed regarding the intelligent manufacturing philosophy of ‘prediction and manufacturing’. First, the key process variables are identified and integrated into the evaluation of the equipment degradation state. Second, the quality deviation index is defined to describe the quality of the product quantitatively according to the co-effect of manufacturing system component reliability and product quality in the quality–reliability chain. Third, to achieve changeable production task demands, mission reliability is defined to characterise the equipment production states comprehensively. The optimal integrated PdM strategy, which combines quality control and mission reliability analysis, is obtained by minimising the total cost. Finally, a case study on decision-making with the integrated PdM strategy for a cylinder head manufacturing system is presented to validate the effectiveness of the proposed method. The final results shows that proposed method achieves approximately 26.02 and 20.54% cost improvement over periodic preventive maintenance and conventional condition-based maintenance respectively.

Sequential Monte Carlo simulation for robust optimal design of cooling water system with quantified uncertainty and reliability

Conventional design of cooling water systems mainly focused on the individual components of cooling water system, not the system as a whole. In this paper, a robust optimal design based on sequential Monte Carlo simulation is proposed to optimize the design of cooling water system. Monte Carlo simulation is used to obtain the cooling load distribution of required accuracy, power consumption and unmet cooling load. Convergence assessment is conducted to terminate the sampling process of Monte Carlo simulation. Under different penalty ratios and repair rates, this proposed design minimizes the annual total cost of cooling water system. A case study of a building in Hong Kong is conducted to demonstrate the design process and test the robust optimal design method. The results show that the minimum total cost could be achieved under various possible cooling load conditions considering the uncertainties of design inputs and reliability of system components.

Reliability Assessment of Gas Pipelines with Corrosion Defects Based on In-Line Inspection

As the natural gas pipeline system in China is extremely complex and busy, people are coming to realize that existing system safety evaluation methods, which are premised on single pipelines or small-scale pipeline networks, are in fact not technical and rational enough for assessing the safety of a large-scale pipeline network. Therefore, ideas for ensuring the reliability of a large-scale natural gas pipeline network have recently been proposed. To calculate system reliability, the primary task is assessing the reliability of individual system components, such as pipelines and gas stations. This paper advances a reliability calculation model based on the Monte Carlo simulation for pipelines with corrosion def ects determined through in-line inspection. An example of the calculation method is provided; in this example, the reliability of a particular PetroChina natural gas pipeline project is ascertained in order to offer some experiences and references for pipeline reliability assessors.

Bayesian Inference of Power System Insulation Reliability in the Presence of Voltage Harmonics

This article illustrates a Bayesian inference methodology for the parametric evaluation of the reliability of power system components in the presence of voltage harmonics. The methodology starts from an experimentally-based life model for solid insulation under distorted voltage, which belongs to the class of the popular Inverse Power Model. Thereafter, a novel approach is developed for building the prior distribution of the unknown life model parameters, described by a joint Normal distribution, that implies a Lognormal model for the hazard rate and the Mean Time To Failure. The applicability and efficiency of the proposed Bayesian methodology is checked by means of a broad amount of Monte Carlo simulations performed for the insulation of power capacitors and cables subjected to harmonic voltages. The results prove the satisfactory behavior of the Bayesian methodology especially for quite small samples; this is an important outcome, since – as well known in the case of real power systems - small samples are a common practical case.

Providing Reliability of Physical Systems: Fully Delay Testable Logical Circuit Design with Compact Representation of all PDF Test Pairs

Functional reliability is one of the important properties of physical systems provided by reliability of system components, in particular, control logical components. The new approach to fully delay testable circuit design oriented to cut overheads and lengths of circuit paths has been developed. Compact representation of all PDF test pairs is reduced to keeping the corresponding generative vector pairs. The number of generative vector pairs does not exceed the doubled number of internal ROBDD nodes originating from the circuit, while the number of the circuit paths can exponentially depend on the number of these internal nodes. The algorithm of involving the PDF test pair from the proper generative vector pair is suggested. This procedure does not require essential calculations. The algorithm of deriving the generative vector pair has a polynomial complexity.